In an earlier account of the workings of the like economy, Gerlitz and Helmond (2013) show how Facebook enables particular forms of social engagement and affective responses through its protocol, collapsing the social with the traceable and marketable and filtering it for positive affects. This project aims to build on this in providing insight into how Reactions buttons, implemented in 2016, relate to this filtering of content that evokes certain emotional reactions.
The introduction of Facebook’s Reactions in 2016 is exemplary for how a platform affords for emotional engagement with content. While still refraining from implementing the Dislike button, which cannot be collapsed in the composite Like counter (Gerlitz and Helmond, 2013), Facebook decided to add buttons, including buttons that portray negativity, mainly angry and sad. This project zooms in on the use of these (negative) buttons in the Syrian news space containing public Facebook pages.
In the commercial endeavour of Facebook, Gerlitz and Helmond (2013) argue, it is only the traceable social that matters to Facebook, as the still intensive, non-measurable, non-visible social is of no actual value for the company: it can neither enter data mining processes nor be scaled up further. Following Gerlitz and Helmond, we should understand Reactions as an attempt to further ‘metrify’ a part of what might have been conceived as non-measurable: emotions evoked in users.
Social buttons both pre-structure and enable the possibilities of expressing affective responses to or engaging with web content, while at the same time measuring and aggregating them (Gerlitz and Helmond, 2013). With implementing the Reactions buttons Facebook is adding several layers to the original like button, extending the ways in which content on Facebook can be qualified in an affective way. The question arises whether the collective use of Reactions, can be 'repurposed' for pinpointng certain publics within a page and for understanding the visual content they engage with.
Figure 3: Schematic overview procedure
Scraped and used in week 1 (link), continued in week 2 for visual content analysis with this set:
Facebook Syrian Revolution Network https://www.facebook.com/Syrian.Revolution/ with 1.937.954 followers was scraped using Netvizz, scraping a time span beginning just before the implementation of Reactions (16 February 2016 - 27 June 2017). Implementation was February 24 2016.
Scraped and analyzed in week 2
Demarcated timeslots to scrape, using time slots derived from three spikes in distinct Reactions usage in the Syrian Revolution Network, see below:
Figure 4 shows the bumpchart of Reactions usage
Across publics within topic:
BBC: https://www.facebook.com/search/top/?q=bbc%20news (42,242,883 followers)
Al Jazeera English: https://www.facebook.com/aljazeera/ (9,984,178 followers)
USA Today: https://www.facebook.com/usatoday/ (8,295,826 followers)
Across pages (other topic, closed communities)
Women's march: https://www.facebook.com/womensmarchonwash/ (783,112 followers)
Black Lives Matter: https://www.facebook.com/BlackLivesMatter/ (282,367 followers)
For scraping we used the page data module, scraping for full stats and posts by page only
Scraping data through Netvizz;
download of pictures through DownThemAll,
attribution of content tags through Google Vision API
descriptives
visual content analysis
T-test to compare means in relation to encoded objects
network analysis with relation to the coded encoded object.
The number of pictures scraped amounted to 552 units, due to the newness of the Facebook page, therefore no samplig was needed to go through all steps (N=552). Some additional steps were added in relation to the first results of the analysis. In particular, Google Vision API showed a significative amount of "text" tags, which reflected the relatively high amount of pictures with text and claims for activism and protest. To futher explore content and compare visual discourse with the Syrian Revolution page, an exploratory analysis of text content was performed usin tag clouds, but we decided not to explore text in depth because of time constraints and less relation to visual analysis.
This protocol is for creating a network of connections between images and the reactions that they generated on Facebook.
Tools used: Netvizz, Google sheet, OpenRefine, Downthemall, Gephi, BBedit, Illustrator
Steps taken:
Phase 01:
Open the full data .tab file in Google Sheet;
Keep only photos, filter out rows with errors.
Phase 02: Get the images
Copy the full_picture column to a new file and save it as image-list.txt;
Open dTa! Manager(part of DownThemAll!) and load the .txt file (Import from file -> make sure the format in the options is set to text files -> Open);
Select all the images -> Select a folder to place them -> in the mask input box type flatcurl (this will rename the images with part of their url);
Download all the images.
Phase 03: Prepare the final dataset
For Gephi to work we would need two separate datasets: a nodes table and an edges table. In order to do that we can use a few Google Sheet functions to make these tables starting from the Netvizz data.
Nodes table (using these header names will save a lot of pain later on)
id | type | amount |
image 01 | image | n° of total reactions |
reaction 01 | reaction | n° of times reaction 01 has been used |
Image 02 | image | n° of total reactions |
Edges table (using these header names will save a lot of pain later)
source | target | weight |
image 01 | reaction 01 | n° of times reaction 01 has been used for image 01 |
image 01 | reaction 02 | n° of times reaction 02 has been used for image 01 |
image 01 | reaction 03 | n° of times reaction 03 has been used for image 01 |
image 02 | reaction 01 | n° of times reaction 01 has been used for image 02 |
Phase 04: Create the network
Import datasets on Gephi;
Spatialize the graph;
Export as svg (it’s important to also export the labels).
Phase 05: Make the final svg
Open the svg in any text editor that has the Find and Replace function;
Remove all the <circle> tag for the images, remove all the <text> tag for the reactions;
Replace the <text> tag for the images with an <image> tag, that will have as attributes: the same x and y attributes of the text tag, an xlink:href attribute with the name of the image, a width and height attribute (1% each and you can always change this value later if the images get too small);
Save the svg and put it in the same folder where all the images are;
Open the svg with Illustrator and make the final touches.
Max | Mean | Std. dev | Count | |
Love | 308 | 19.94 | 35.15 | 7237 |
Wow | 31 | 1.23 | 2.76 | 445 |
Haha | 193 | 5.90 | 21.18 | 2142 |
Sad | 1278 | 56.63 | 141.1 | 20557 |
Angry | 339 | 19.28 | 43.23 | 6998 |
Love | Wow | Haha | Sad | Angry | |
Love | 1 | ||||
Wow | .349, p = .000 | 1 | |||
Haha | .179, p = .001 | .350, p = .000 | 1 | ||
Sad | .059, p = .261 | .044, p = .403 | -.088, p = .095 | 1 | |
Angry | -.159, p = .003 | .017, p = .752 | -.083, p = .114 | .703, p = .000 | 1 |
Anger | Fear | Sadness | Shame | Contentment | Happiness | |
Anger | 1 | |||||
Fear | .007, p = .896 | 1 | ||||
Sadness | .073, p = .167 | .275, p = .000 | 1 | |||
Shame | .108, p = .040 | -.013, p = .812 | .078, p = .138 | 1 | ||
Contentment | .013, p = .799 | -.077, p = .146 | -.097, p = .065 | .065, p = .217 | 1 | |
Happiness | . 044, p = .406 | -.052, p = .328 | -.123, p = .020 | .110, p = .036 | .097, p = .066 | 1 |
Love | Wow | Haha | Sad | Angry | ||
Male | T = -3.026, p = .003 | T = .732, p = .465 | T = -.956, p = .340 | T = -1.310, p = .191 | T = 1.663, p = .097 | |
M = 13.82, SD = 31.45 | M = 1.34, SD = 3.09 | M = 4.72, SD = 14.06 | M = 45.90, SD = 101.46 | M = 23.45, SD = 49.00 | ||
1 | M = 24.92, SD = 37.24 | M = 1.13, SD = 2.47 | M = 6.86, SD = 25.55 | M = 65.38, SD = 166.32 | M = 15.88, SD = 37.67 | |
Female | T = -.254, p = .800 | T = 1.282, p = .201 | T = 1.460, p = .145 | T = -.609, p = .543 | T = -.211, p = .833 | |
M = 19.78, SD = 35.96 | M = 1.29, SD = 2.89 | M = 6.43, SD = 22.21 | M = 55.16, SD = 143.37 | M = 19.12, SD = 43.87 | ||
1 | M = 21.37, SD = 26.77 | M = .66, SD = .94 | M = .94, SD = 1.41 | M = 70.46, SD = 118.55 | M = 20.74, SD = 37.18 | |
Child | T = 1.137, p = .256 | T = 1.712, p = .088 | T = 1.712, p = .088 | T = -4.138, p = .000 | T = -3.450, p = .001 | |
M = 21.08, SD = 38.03 | M = 1.36, SD = 3.08 | M = 1.36, SD = 3.08 | M = 40.31, SD = 106.61 | M = 15.08, SD = 32.16 | ||
1 | M = 16.08, SD = 22.67 | M = .77, SD = 1.10 | M = .77, SD = 1.10 | M = 111.70, SD = 212.69 | M = 33.45, SD = 66.84 | |
Text | T = 2.744, p = .006 | T = 1.512, p = .132 | T = 1.648, p = .100 | T = 2.517, p = .012 | T = .970, p = .333 | |
M = 22.52, SD = 37.80 | M = 1.34, SD = 3.01 | M = 6.84, SD = 23.54 | M = 66.27, SD = 156.02 | M = 20.46, SD = 46.17 | ||
1 | M = 10.11, SD = 19.96 | M = .8, SD = 1.47 | M = 2.32, SD = 6.00 | M = 20.49, SD = 39.72 | M = 15.01, SD = 29.57 | |
Signholder | T = 1.34, p = .182 | T = 2.228, p = .027 | T = 2.339, p = .020 | T = 2.520, p = .012 | T = 3.293, p = .001 | |
M = 21.15, SD = 37.46 | M = 1.39, SD = 3.02 | M = 7.16, SD = 23.39 | M = 65.80, SD = 154.14 | M = 22.92, SD = 47.03 | ||
1 | M = 14.86, SD = 22.79 | M = .57, SD = .93 | M = .57, SD = 1.14 | M = 18.52, SD = 45.57 | M = 4.10, SD = 12.37 |
Table 1: Shares and reactions across news pages
Maximum | Mean | Std. Deviation | Total | |
Share_Count | 209718 | 2462.64 | 17536.676 | |
Love | 684 | 105.44 | 140.434 | 2040 |
Wow | 3553 | 106.66 | 354.280 | 2517 |
Haha | 583 | 33.53 | 62.154 | 1566 |
Sad | 66413 | 1229.74 | 5742.020 | 110990 |
Angry | 32559 | 645.56 | 2828.217 | 56195 |
Valid N (listwise) | 144 |
Maximum | Mean | Std. Deviation | Total | |
Share_Count | 209718 | 3806.60 | 24483.370 | |
Love | 479 | 34.51 | 69.498 | 1180 |
Wow | 687 | 37.47 | 110.764 | 2086 |
Haha | 583 | 22.60 | 70.288 | 1150 |
Sad | 66413 | 1593.63 | 7776.848 | 82554 |
Angry | 32559 | 807.73 | 3805.112 | 47411 |
Valid N (listwise) | 73 |
Maximum | Mean | Std. Deviation | Total | |
Share_Count | 16721 | 1603.68 | 3262.358 | |
Love | 684 | 105.21 | 138.977 | 3998 |
Wow | 3553 | 208.13 | 606.188 | 7909 |
Haha | 264 | 48.21 | 62.655 | 1832 |
Sad | 14057 | 1352.24 | 2927.905 | 51385 |
Angry | 7581 | 819.13 | 1532.025 | 31127 |
Valid N (listwise) | 38 |
Minimum | Maximum | Mean | Std. Deviation | Total | |
Share_Count | 3 | 11883 | 478.73 | 2050.816 | |
Love | 2 | 622 | 262.61 | 134.089 | 8666 |
Wow | 2 | 1700 | 142.88 | 289.666 | 4715 |
Haha | 0 | 174 | 40.82 | 32.025 | 1347 |
Sad | 14 | 1300 | 283.70 | 323.608 | 9362 |
Angry | 10 | 1100 | 86.94 | 189.264 | 2869 |
Valid N (listwise) | 33 |
The differences could also be found in the correlations tables. Tables 5 - 7 show the correlation between the different reaction buttons in the three pages.
Table 5: Al Jazeera English reaction correlations Love | Wow | Haha | Sad | Angry | |
Love | 1 | ||||
Wow | .526** | 1 | |||
Haha | .264* | .280* | 1 | ||
Sad | .384** | .675** | 0.162 | 1 | |
Angry | .394** | .716** | 0.173 | .994** | 1 |
**. Correlation is significant at the 0.01 level (2-tailed). |
Table 5 shows similar correlations to those which were found in the Syrian Revolution page. Two different findings that can be found are the very high correlation (higher than in the Syrian revolution page) between sad and angry, making this correlation almost perfect (r=.994, p<.01), and a new correlation beween the use of Wow and angry reaction buttons (r=.716, p>.01).
Table 6: BBC reaction correlations Love | Wow | Haha | Sad | Angry | |
Love | 1 | ||||
Wow | .434** | 1 | |||
Haha | 0.065 | 0.295 | 1 | ||
Sad | -0.082 | 0.069 | 0.039 | 1 | |
Angry | 0.179 | .572** | 0.183 | .654** | 1 |
**. Correlation is significant at the 0.01 level (2-tailed). |
Table 6 shows that the correlation between Wow and angry that was found in the arab news community (Al Jazeera) page appears also in the western broadsheet outlet (r=.572, p<.01). Sad and angry were also correlated, but in a correlation which is similar to the one which was found in the Syrian revolution page (r=.654, p<.01).
Table 7: USA Today reaction correlations Love | Wow | Haha | Sad | Angry | |
Love | 1 | ||||
Wow | .632** | 1 | |||
Haha | .636** | 0.294 | 1 | ||
Sad | -0.178 | 0.016 | -0.309 | 1 | |
Angry | .434* | .922** | 0.157 | 0.291 | 1 |
Table 8: Content analysis descriptives
Count | |
Male | 95 |
Female | 29 |
Child | 43 |
None | 34 |
Text | 6 |
Signholder | 1 |
Similarly to the Syrian revolution page, there was a high representation of male characters (95) and children (25), along with female characters (29). However, while in the Syrian Revolution page there were lots of sign holders, in the news outlets they appeared only once. Table 9 shows an independent samples t-test for the differences between posts that included certain objects and those we did not.
Table 9: T-tests for the presence of certain image content and reactions
Love | Wow | Haha | Sad | Angry | ||
Male | T = 4.145, p < .001 | T = 2.756, p = .007 | ||||
No | M = 170.21, SD = 184.94 | M = 222.28, SD = 591.14 | ||||
Yes | M = 71.52, SD = 99.05 | M = 50.92, SD = 100.05 | ||||
Female | Not significant | |||||
No | ||||||
Yes | ||||||
Child | T = -2.407, p = .017 | |||||
No | M = 482.67, SD = 1554.53 | |||||
Yes | M = 2982.65, SD = 10106.13 | |||||
Text | T = -5.411, p < .001 | |||||
No | M = 76.59, SD = 199.79 | |||||
Yes | M = 811.33, SD = 1374.63 | |||||
Signholder | Not significant | |||||
No | ||||||
Yes |
Table 9 shows that posts that included male objects got less love and wow reactions compared to those to did not have male objects (t=-.331, p<.01; t=-.227, p<.01), while posts who included no human characteristics at all received more love and wow reactions compared to those who included human characteristics (t=.282, p<.01; t=.294, p<.01). Posts that included text received more Wow reactions (t=.416, p<.01) and posts which included children received more sad reactions (t=.199, p<.01).
Network AnalysisFigure 8 shows how the different images clustered around the different reaction buttons. The size of the reaction face represents its centrality in the network.
Figure 9. Network Analysis of pictures' clusters in Al Jazeera English, BBC News and USA Today.The network shows that most images clustered between sad and angry, showing their large correlation. The purple circle in the figure shows unique pictures in Al Jazeera English, while the red circle shows unique pictures of USA Today. Despite the fact that those picture are on the opposite site of the network - a close examination of them show that they were similar in content.
Another interesting finding can be seen in Figure C, showing the network of each of the news outlets separately, showing that while Al Jazeera English and BBC News had many shared spaces, USA Today's pictures tended to cluster around unique reactions.
Figure 10. Network Analysis of pictures' clusters in Al Jazeera English, BBC News and USA Today - separated.Since it's conception in December 2016, there has been a lot of engagement with the Women's March group through reactions and likes, but not that many comments. The highest average reaction (aside from 'like') was love. In fact, love reactions dominated this page, with the secondary most frequent reaction being sad, although love reactions occured 12x more on average than sad ones. Table 9 shows the full descriptive statistics for the page engagement, whereas table 10 shows the descriptive statistics for the reactions.
Table 10: Women's March Descriptive statistics Maximum | Mean | Std. Deviation | |
likes | 91776 | 4181.57 | 8155.965 |
comments | 5532 | 228.34 | 506.633 |
reactions | 121004 | 5053.09 | 10072.007 |
shares | 35859 | 1002.30 | 2560.885 |
engagement | 160521 | 6283.72 | 12569.034 |
Maximum | Mean | Std. Deviation | |
Like | 90727 | 4126.14 | 8055.014 |
Love | 27416 | 679.57 | 1774.395 |
Wow | 1239 | 20.24 | 102.806 |
Haha | 2973 | 11.46 | 129.627 |
Sad | 9496 | 70.44 | 496.503 |
Angry | 16839 | 51.44 | 727.820 |
Thankful | 930 | 2.24 | 39.942 |
Similar to the Syrian datasets, likes way outnumber all the other reactions. Hence once more we consider it to be too ambigious to be an emotional reaction. Another interesting point was is the enormous deviation of angry reactions from the mean, indicating that there is an outlying post that caused a lot of angry reactions, although these posts do not seem to be the norm.
Figure 10: Correlations between emotions over time
Three combinations of emotions were found to be significant, which can be seen in Figure D, and was also confirmed by Pearson's R correlation tests. Similar to the Syrian Revolution page, there is a very strong positive correlation between sad and angry (r = .815, p =.000). There is also a very strong positive correlation between love and wow (r = .7, p = .000). Lastly, there is a weak positive correlation between love and haha (r = .117, p = .006). For instance, the correlation between love and wow is apparent at the end of january, and the correlation between sadness and anger occurs in the middle of January. Therefore, similar combinations of emotions can be observed across issues, publics and cultures. Lastly, there is a big spike of the thankful reaction which can be observed in mid-may, although this button is not always available, which could explain why it has no use in other time frames.
Figure 12: Image tags from the Women's MarchUnlike the Syrian Revolution facebook group, the image tags for text and font were very relevant and accurate for the Women's March group (see Fig. E). Tag frequencies were counted using the tool on writewords.org.uk. Most images in the group were just text promoting marches and ideas of solidary amongst women. This shows the different way that people use images on a platform like facebook to spread short messages. Images grab more attention than the text description under it, especially if the message is short and wants to be disseminated easily. Sharing an image is easier than copy-pasting a status. Therefore, the tags of font, text and poster were very accurate to describe the images on the Women's March facebook group.
However, examining the emotional reactions ot the tags did not make sense, because of the different use of images on this page. Hence, a visual analysis not really a useful method of analysis. Therefore, we found it would tell us more if we examined the words present within these images and their association with the dominant emotional reaction. So we used the Optical Character Recognition (OCR) feature of Google’s Vision API to examine the text within the images, which could perhaps tell us more of the motivations of reactions to certain images.
Figure 13: Tag cloud for words within images receiving sad reactionsAs an example, Figure F shows some of the most prominent words that recieved 'sad' reactions, extracted from images with OCR. At face value, this is already far more meaningful to exaplain the abundance of sad reactions than the tag 'text' or 'font'. Consequently, we recommend for groups which use images for spreading textual messages to extract the text from those images in order to draw meaningful conclusions about the content. Word frequencies as well as N-grams are useful for examining the text, however for the sake of brevity (and that this summer school has a visual theme) we have left the further analysis out.
Overall, we discovered that the Women's March page was used mainly as a call to action, and the images are simply reappropriated as ways to spread textual messages.
Content analysis Women's MarchCoding was executed on the visual data in a similar way to the coding of the images in the Syrian Revolution Network. As the dataset was not toot big, we did not take a sample. We coded 553 images for Anger, Fear, Sadness, Shame, Contentment, Happiness, Male, Female, Child, Unknown, None, Text, Sign Holder and Other stuff (the latter was basically used to code for cartoon).
_Table 12 shows the count for the content analysis of Women's March (we left out emotions, as it turned out to be too unstable)._ Count | |
Male | 85 |
Female | 255 |
Child | 57 |
Unknown | 58 |
None | 247 |
Text | 392 |
Sign Holder | 87 |
Cartoons | 123 |
Results of the content analysis were used to test if some objects or subjects were more associated with particular emotional reactions. We cross-checked this aspect using both t-test and network analysis. Both of them gave no evidence of particular association of sentiments with subjects. This was due to the relatively dominant presence of "love" reactions, which were able to attract the majority of relevant subjects: women, children and signholders.
The Black Lives Matter Facebook group The Black Lives matter was scraped for the same time frame as the Women's march (December 2016 - July 2017), resulting in N = 134 of image only posts. Once again, there were more reactions (M = 1412, SD = 2757) than comments (M = 95, SD = 149) or shares (M = 341, SD = 1110). Therefore, people comment less than they react, and also do not share much. Also similarly to the Women's March page, on average most post recieved the love reaction (M = 169, SD = 383). The second most common reaction on average was sad (M = 66, SD = 380). Examinig the correlations between emotions shows that results from the Syrian Revolution and Women's March pages are once again replicated. There is almost a perfect correlation between sad and angry (r = 958, p = .000), a moderate correlation between love and haha (r = .53, p = .000) and a weak to moderate correlation between love and wow (r = .405, p = .000), and love and thankful (r = .408, p = .000). Therefore we repeatedly saw a clear emergence of positive/negative emotions, but people express the dominant emotion different.y Again, we see the rise of more complex emotions that can be categorized on a positive - negative spectrum.On the methods
Mining Facebook reactions can show interesting emergent, complex emotions from groups. The emotions can be clearly polarized, where different expressions of emotional reactions used together (such as sad and angry) can show a negative overall mood, whereas happy and love can show an overall positive mood. The thankful button is not too useful to examine, because it is not always available and seems to be used infrequently. It might be interesting if you know the exact time span that Facebook makes it available, otherwise you don’t know if people aren’t replying thankful because they don’t feel that way, or if the button simply wasn’t available. Across the board, the "like" button is too ambiguous and does not necessarily represent an emotional reaction (although, it can). So it’s quite difficult to include it when examining emotional reactions, as it lends itself to misguided conclusions. Finally, many online tools are readily available and free to use in order to conduct this type of analysis.
On the emotional correlations
Across all pages the wow button was used, whether to express it together with something positive (haha) or something negative (anger), except in the case of the Syrian Revolution facebook group. Hence it is clear that the closer the group is to the violent event (in this case, the Syrian war), there is no expression of shock or surprise. This could be due to the constant exposure and geographic closeness of the group to the events, which therefore do not result in any surprise. That said, only images were examined, and hence there might be differences when taking into account textual messages also.
Interestingly when examining the Syrian news space, there was a very strong positive correlation between angry and wow that emerged. This correlation was especially strong in the USA Today audience, which interestingly was the only audience that did not experience a plurality of anger and sadness. Therefore, with this audience we see a completely different collective expression (or lack thereof). This may be due to either the specific audience or overall culture.
Both the Women’s March and Black lives matter pages showed the same correlations between emotions as the Syrian Revolution page and Syrian news space; Angry and sad (frustration), love and wow (enthrallment), love and haha (general happiness). There was also an additional correlation in the Black Lives Matter page between thankful and love, although the use of the thankful button is not always possible, and therefore its interpretation should be taken cautiously. In the future, one should consider regrouping the variables into groups of correlated emotions. This will increase accuracy of conclusions when examining reactions to images, since the pluarlity of emotions when using the reaction button is an important consideration.
Future questions relate to questions about:
Whether we can pinpoint certain communities in pages that serve a more or less diverse audience.
Does the use of Reactions within a cluster of 'objects' that appears, point to a certain group. For example within the child cluster, does the use of the sad button connect to a different public than the use of the angy button?
When comparing the overall news reactions to the Syrian revolution page, there are some similar emotional correlations to be found, including; Angry and sad, Love and wow as well as Love and haha. The key difference is that the news sites showed a strong positive correlation between wow and angry, as well as sad and wow. Compared to the Syrian revolution page, there was not a lot of use of the 'wow' button, hinting at the lack of surprise of the events in Syria in this community. Moreover, the lack of the use of the haha reaction in the news sites indicate that there was nothing funny being reported about Syria.
In addition, although there were some similarities between the news communities, it is important to examine the nuances based on the different news outlets. The lack of correlation between sad and angry, in favour of a strong positive correlation between wow and angry shows that there is a clear difference the way that news is presented and recieved amongst audiences.
Overall, there is a clear demarcation between positive and negative emotions when examining images across topics, pages and communities.
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